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reaktiv

reaktiv: Reactive Signals for Python

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Reactive declarative state management for Python — automatic dependency tracking and reactive updates for your application state.

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What is reaktiv?

Section titled “What is reaktiv?”

reaktiv is a reactive declarative state management library for Python that lets you declare relationships between your data instead of manually wiring updates. When data changes, everything that depends on it updates automatically — eliminating a whole class of bugs where you forget to update dependent state.

Think of it as a spreadsheet for your Python application: change a cell, and all formulas using that cell recalculate instantly.

Why Use reaktiv?

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reaktiv solves common pain points in state management:

  • Eliminates manual state synchronization - No more forgetting to update derived values
  • Reduces bugs - Ensures consistent state throughout your application
  • Simplifies code - Declare relationships once, not every time data changes
  • Improves performance - Only recomputes what actually needs to change

Learn more about why you should use reaktiv →

Features

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  • Automatic state propagation: Change a value once, and all dependent computations update automatically
  • Efficient updates: Only the necessary parts are recomputed (fine‑grained reactivity)
  • Zero external dependencies: Lightweight and easy to incorporate into any project
  • Type-safe: Fully annotated for clarity and maintainability
  • Lazy and memoized: Computations run only when needed and cache until dependencies change

Quick Start

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reaktiv is built around core primitives for reactive programming:

  1. Signals: Store values and notify dependents when they change
  2. Computed Signals: Derive values that automatically update when dependencies change
  3. Effects: Run side effects when signals or computed signals change
from reaktiv import Signal, Computed, Effect


# Reactive data sources
name = Signal("Alice")
age = Signal(30)


# Reactive derived value
greeting = Computed(lambda: f"Hello, {name()}! You are {age()} years old.")


# Reactive side effect (retain a reference!)
greeting_effect = Effect(lambda: print(f"Updated: {greeting()}"))


# Update base data — everything reacts automatically
name.set("Bob")   # Prints: Updated: Hello, Bob! You are 30 years old.
age.set(31)       # Prints: Updated: Hello, Bob! You are 31 years old.

Documentation

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